In this work, the operational production scheduling problem of a manufacturer in the automotive sector, producing injection molded parts, is presented. In order to meet all requirements, including alternative resource...
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In this work, the operational production scheduling problem of a manufacturer in the automotive sector, producing injection molded parts, is presented. In order to meet all requirements, including alternative resources, release dates, due dates and sequence dependent setup times, a schedule classification and a related integer programming formulation for this flexible job-shop scheduling real-world problem is presented. Since the combinatorial complexity of the problem does not allow an efficient optimization for the company partner, a constraint programming approach is proposed, solving the real-world case to optimality within a few seconds of runtime.
We investigate the production planning and detailed scheduling of multiple-stage flexible flow shops, making products that require different production cycles. The production process can be configured in several ways,...
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We investigate the production planning and detailed scheduling of multiple-stage flexible flow shops, making products that require different production cycles. The production process can be configured in several ways, involving both different processing phases and times, depending on specific treatments required by the processed job. Jobs require operations on a unique raw material item and its quality features can require specific processing phases as well as lead to different processing times. We investigate, through the succession of a MILP and a CP model, the impact of quality-related aspects on processing times and hence on the overall planning and scheduling problem. An actual case from a leather tannery industry derived from the M2H – Machine To Human” (project code CBYX592), INNONETWORK 2017, Regione Puglia is investigated.
Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots i...
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Project scheduling in manufacturing environments often requires flexibility in terms of the selection and the exact length of alternative production activities. Moreover, the simultaneous scheduling of multiple lots is mandatory in many production planning applications. To meet these requirements, a new resource-constrained project scheduling problem (RCPSP) is introduced where both decisions (activity flexibility and time flexibility) are integrated. Besides the minimization of makespan, two new alternative objectives are presented: maximization of balanced length of selected activities (time balance) and maximization of balanced resource utilization (resource balance). New mixed integer and constraint programming (CP) models are proposed for the developed integrated flexible project scheduling problem. Benchmark instances on an already existing flexible RCPSP and the newly developed problem are solved to optimality. The real-world applicability of the suggested CP models is shown by additionally solving a large industry case.
This paper presents a method for the multiple autonomous vehicles mission flight planning in changing weather conditions. We model UAVs fleet servicing spatially-dispersed customers in terms of declarative modelling f...
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This paper presents a method for the multiple autonomous vehicles mission flight planning in changing weather conditions. We model UAVs fleet servicing spatially-dispersed customers in terms of declarative modelling framework. The considered problem boils down to a predictive and reactive planning of delivery missions within a specified timeframe. Due to the need to implement an emergency return of a UAV to its base, or to handle variations in delivery periods, conditions sufficient to allow eliminating unfeasible solutions, and thus allowing to speed up the calculations, have been developed. The results of numerous computer experiments have confirmed experiments these expectations.
constraint answer set programming (CASP) is a family of hybrid approaches integrating answer set programming (ASP) and constraint programming (CP). These hybrid approaches have already proven to be successful in vario...
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The importance of computer-aided process planning (CAPP) for assembly is widely recognized, as it holds the promise of efficient and automated construction of solutions for a complex, geometrically, technologically, a...
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The importance of computer-aided process planning (CAPP) for assembly is widely recognized, as it holds the promise of efficient and automated construction of solutions for a complex, geometrically, technologically, and economically constrained planning problem. This complexity led to the introduction of decomposition approaches, separating the macro-level planning problem that oversees the complete assembly process from the various micro-level problems that look into the details of individual assembly operations. The paper introduces a constraint model for solving the macro-level assembly planning problem based on a generic feature-based representation of the product and the assembly operations involved. Special attention is given to capturing the feedback from micro-level planners expressed in the form of feasibility cuts, and hence, to the integration of the approach into a complete CAPP workflow. Results on three case studies from different industries are also presented to illustrate the practical applicability of the approach.
We study a variant of the multiprocessor job scheduling problem, where jobs are processed by several identical machines. The machines are ordered in a sequence, and each job is processed by several consecutive machine...
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We study a variant of the multiprocessor job scheduling problem, where jobs are processed by several identical machines. The machines are ordered in a sequence, and each job is processed by several consecutive machines simultaneously. The jobs are characterized by their processing time, the number of required consecutive machines, and their ready time. The objective function is to minimize the sum of general functions defined over the completion time of each job. This study is motivated by a real problem in the semiconductor industry. We present a time-indexed integer programming and a constraint programming formulations for the problem and demonstrate their applicability through an extensive numerical study and an industrial case study. (C) 2020 Elsevier B.V. All rights reserved.
This paper studies a simultaneous scheduling of production and material transfer in a flexible job shop environment. The simultaneous scheduling approach has been recently adopted by a robotic mobile fulfillment syste...
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This paper studies a simultaneous scheduling of production and material transfer in a flexible job shop environment. The simultaneous scheduling approach has been recently adopted by a robotic mobile fulfillment system, wherein transbots pick up jobs and deliver to pick-stations for processing, which requires a simultaneous scheduling of jobs, transbots, and stations. Two different constraint programming formulations are proposed for the first time for a flexible job shop scheduling problem with transbots, significantly outperforming all other benchmark approaches in the literature and proving optimality of the well-known benchmark instances.
Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we ...
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Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the "due times rule", the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.
This paper describes a unified global constraint to model scheduling problems with unary resources, i.e., each resource can process only a single activity at a time. In addition, the constraint enforces sequence-depen...
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This paper describes a unified global constraint to model scheduling problems with unary resources, i.e., each resource can process only a single activity at a time. In addition, the constraint enforces sequence-dependent transition times between activities. It often happens that activities are grouped into families with zero transition times within a family. Moreover, some of the activities might be optional from the resource viewpoint (typically in the case of alternative resources). The global constraint unifies reasoning with both optional activities and families of activities. The scalable filtering algorithms we discuss keep a low time complexity of O(n center dot log(n)center dot log(f)), where n is the number of tasks on the resource and f is the number of families. This results from the fact that we extend the Theta-tree data structure used for the Unary Resource constraint without transition times. Our experiments demonstrate that our global constraint strengthens the pruning of domains as compared with existing approaches, leading to important speedups. Moreover, our low time complexity allows maintaining a small overhead, even for large instances. These conclusions are particularly true when optional activities are present in the problem.
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